Optimization is an inherent part of design for complex physical systems. Often optimization problems are posed as noisy black-box problems subject to constraints, where each function call requires an extremely expensive computation or a physical experiment. A large range of these problems requires optimizing a mixed variables design space (combinatorial, discrete, and continuous) for multiple objectives. Architected material design [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, hyper-parameter tuning for machine learning algorithms [7]}, [8]}, [9]}, drug design [10]}, [11]}, controller sensor placement [12]} pose such problems and Bayesian optimization is a natural candidate for their optimization.
